[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3909":3,"related-3909":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":25,"secondary_topics":30,"classification":31,"search_phrases":57,"slug":60,"view_count":61,"doi":62,"paper":63,"created_at":85},3909,"Principles of creating a National Spatial Data System","https:\u002F\u002Fdoi.org\u002F10.18799\u002F24131830\u002F2026\u002F9\u002F5868","The article analyzes in detail the processes of formation and development of the National Spatial Data System – an open system and the most important tool for the digitalization and systematization of open spatial data in Russia. Spatial information is of great importance for optimization of public administration processes (in the sphere of land and property relations), planning of territorial use, implementation of infrastructure projects, cadastral accounting, as well as for meeting the needs of the population, business and government agencies in the field of ecology, defense, agriculture, forestry and other industries. The authors focus on the importance of creating an integrated digital resource where real estate information cartographic data, and territorial planning will be collected on a single multifunctional platform. The paper considers in detail the goals and objectives of the state program for the creation of the National Spatial Data System. The development of domestic geoinformation software, increasing the completeness and quality of data in the Unified State Register of Real Estate and other information systems, achieving a high level of digital maturity, and improving the quality of public services are among them. The study presents the results of the system operation in four pilot regions: Perm Krai, the Republic of Tatarstan, Krasnodar Krai, and Irkutsk Oblast. The paper describes the obtained results , including the elimination of errors in the boundary register, data verification and provision of services for citizens, businesses, and government agencies. The National Spatial Data System is considered as the basis for building a unified geoinformation ecosystem, including analytics, forecasting, automated services and decision support. The paper introduces the key areas of interdepartmental interaction and technical integration. The authors make a conclusion on the project high significance for the implementation of the country spatial development strategy, including territorial planning, and for increasing the investment attractiveness of constituent entity of the Russian Federation. For citation: Martynova E.V., Balandin V.A., Lipilin D.A., Gura D.A. Principles of creating a National Spatial Data System. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 9, pp. 190–199. http:\u002F\u002Fdoi.org\u002F10.18799\u002F24131830\u002F2026\u002F9\u002F5868","本文详细分析了国家空间数据系统（National Spatial Data System）的形成与发展过程——这是一个开放系统，也是俄罗斯开放空间数据数字化和系统化的重要工具。空间信息对于优化公共管理流程（土地与财产关系领域）、领土利用规划、基础设施项目实施、地籍核算，以及满足居民、企业和政府机构在生态、国防、农业、林业等行业的需求具有重要意义。作者重点关注创建一体化数字资源的重要性，即将不动产信息、制图数据和领土规划汇集于一个多功能平台。本文详细探讨了创建国家空间数据系统的国家计划的目标与任务，其中包括：发展国产地理信息软件，提高统一国家不动产登记簿及其他信息系统中数据的完整性和质量，达到高水平的数字化成熟度，以及提升公共服务质量。研究展示了该系统在四个试点地区（彼尔姆边疆区、鞑靼斯坦共和国、克拉斯诺达尔边疆区和伊尔库茨克州）的运行结果。本文描述了所取得的成果，包括消除边界登记中的错误、数据核查以及为公民、企业和政府机构提供服务。国家空间数据系统被视为构建统一地理信息生态系统的基础，涵盖分析、预测、自动化服务和决策支持。本文介绍了部门间协作和技术集成的关键方向。作者得出结论：该项目对于实施国家空间发展战略（包括领土规划）以及提高俄罗斯联邦主体投资吸引力具有重大意义。引用格式：Martynova E.V., Balandin V.A., Lipilin D.A., Gura D.A. Principles of creating a National Spatial Data System. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 9, pp. 190–199. http:\u002F\u002Fdoi.org\u002F10.18799\u002F24131830\u002F2026\u002F9\u002F5868",null,"Bulletin of the Tomsk Polytechnic University Geo Assets Engineering","2026-09-30T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"俄罗斯国家空间数据系统论文，属测绘地理信息与数字治理范畴，与三农、农业信息化无直接关联，不予入选。",[19],{"name":10,"url":6},[21,22,23,24],"数字治理","地理信息","空间数据系统","不动产登记",{"code":26,"slug":27,"name":28,"path":29,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"partial","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":40,"reason":56},[37,45,50],{"code":38,"score":39,"supported":40,"title":41,"abstract":42,"tags":44},"I2",3,true,[],[43],"decision support",[],{"code":46,"score":39,"supported":40,"title":47,"abstract":48,"tags":49},"I4",[],[22],[],{"code":51,"score":52,"supported":14,"title":53,"abstract":54,"tags":55},"G5",1,[],[],[21],"一级领域明确，二级方向需要细分",[58,59],"俄罗斯 国家空间数据系统","统一不动产登记 地理信息","俄罗斯国家空间数据系统-3909",2,"10.18799\u002F24131830\u002F2026\u002F9\u002F5868",{"doi":62,"openalex_id":64,"authors":65,"venue":10,"cited_by_count":15,"oa_url":6,"card":78,"direction":82,"ingested_from":84},"W7214930429",[66,69,72,75],{"name":67,"orcid":68},"Elena V. Martynova","https:\u002F\u002Forcid.org\u002F0009-0007-3068-887X",{"name":70,"orcid":71},"Vasily A. Balandin","https:\u002F\u002Forcid.org\u002F0009-0009-3175-3252",{"name":73,"orcid":74},"Dmitry A. Lipilin","https:\u002F\u002Forcid.org\u002F0000-0002-3766-6063",{"name":76,"orcid":77},"Dmitry A. Gura","https:\u002F\u002Forcid.org\u002F0000-0002-2748-9622",{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"分析俄罗斯国家空间数据系统的构建原则、目标及四个试点地区运行成效。","分析国家空间数据系统项目文件与四个试点地区运行结果。","该系统是统一地理信息生态基础，可支撑决策、规划并提升投资吸引力。","数字乡村与农业信息化","可研究国家空间数据平台如何与农业用地管理、确权及乡村规划数据对接。","openalex","2026-10-01T23:30:39.457998Z",{"total":87,"page":52,"page_size":87,"items":88},6,[89,174,266,328,403,477],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":98,"sources":104,"tags":106,"primary_topic":112,"secondary_topics":113,"classification":114,"search_phrases":156,"slug":159,"view_count":15,"doi":160,"paper":161,"created_at":173},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",56,{"impact":99,"substance":100,"depth":101,"authority":87,"freshness":102,"relevant":52,"comment":103},12,16,13,9,"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[105],{"name":95,"url":92},[107,108,109,110,111],"智慧农业","农业人工智能","遥感监测","风险管理","病虫害监测",{"code":26,"slug":27,"name":28,"path":29,"parent_code":9},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":115},{"candidates":116,"agricultural_evidence":40,"reason":56},[117,124,133,141,149],{"code":38,"score":13,"supported":40,"title":118,"abstract":119,"tags":123},[],[120,121,122,43],"机器学习","machine learning","artificial intelligence",[108],{"code":125,"score":13,"supported":40,"title":126,"abstract":127,"tags":132},"I3",[],[128,129,130,131],"遥感","remote sensing","multispectral","hyperspectral",[109],{"code":134,"score":102,"supported":40,"title":135,"abstract":136,"tags":140},"I5",[],[137,138,139],"物联网","internet of things","IoT",[],{"code":142,"score":143,"supported":40,"title":144,"abstract":145,"tags":148},"I1",7,[],[146,147],"精准农业","precision agriculture",[107],{"code":150,"score":87,"supported":40,"title":151,"abstract":152,"tags":155},"H7",[],[153,154],"气候变化","climate change",[],[157,158],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":160,"openalex_id":162,"authors":163,"venue":95,"cited_by_count":15,"oa_url":92,"card":166,"direction":172,"ingested_from":84},"W7223085400",[164],{"name":165,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":188,"tags":190,"primary_topic":192,"secondary_topics":193,"classification":194,"search_phrases":252,"slug":255,"view_count":15,"doi":256,"paper":257,"created_at":265},5556,"AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.76s.32","Weeds, pest attacks, labour shortage and over-dependence on chemical pesticides are the serious challenges faced by the agricultural sector in the wake of ever growing weed infestation.Manual weed and pest control has potential negative environmental impacts, increased production costs and reduced yields.The application of Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, robotics, and laser technologies are a promising way of developing sustainable and autonomous agricultural systems.This study introduces an intelligent agricultural system based on Artificial Intelligence (AI) and Internet of Things (IoT) for weed identification, harmful insect identification, crop condition monitoring and automatic weed removal using a laser beam.The system will be based on Internet of Things (IoT) sensors, Computer vision modules, Convolutional neural networks (CNN), Unmanned robotic platforms, Environmental sensing systems, and Electronic Swarm Suppression (ESS) mechanisms to enhance agricultural productivity and reduce crop losses.The system uses deep learning algorithms for real time weed, crop, beneficial and harmful insect classification.High-resolution cameras and sensing devices collect environmental and crop data continuously and feed the data into an IoT system for processing in a cloud-edge computing system.High precision weed elimination with a robotic platform containing laser emitters which do not harm adjacent crops.Further, computer vision and environmental monitoring are employed to identify aerial insect swarms and to enable automatic attacks of insect swarms by means of a laser beam and ESS technology.Multiple agricultural blocks were used for experimental evaluation of the maize, wheat, soybean and vegetable crops.The results show substantial increases in the accuracy of weed detection, pest identification, crop protection and cost savings for operations over current practices.The proposed system achieved a 97.6% accuracy in the weed classification, 96.8% accuracy in the detection of the insect and a 42.3% reduction in pesticide use.In addition, the crop yield rose by 18.7% and the operational costs dropped by 28.4%.Overall, the results suggest that AI-powered IoT can significantly enhance precision agriculture and promote sustainable food production.","在杂草侵扰日益加剧的背景下，杂草、病虫害侵袭、劳动力短缺以及过度依赖化学农药是农业部门面临的严峻挑战。人工除草和病虫害防治具有潜在的环境负面影响，会增加生产成本并降低产量。人工智能（AI）、物联网（IoT）、深度学习、机器人技术及激光技术的应用，是发展可持续自主农业系统的有前景的途径。本研究提出一种基于人工智能（AI）和物联网（IoT）的智能农业系统，用于杂草识别、有害昆虫识别、作物状况监测以及利用激光束自动除草。该系统将基于物联网（IoT）传感器、计算机视觉模块、卷积神经网络（CNN）、无人机器人平台、环境感知系统及电子蜂群抑制（ESS）机制，以提高农业生产力并减少作物损失。该系统采用深度学习算法对杂草、作物、有益昆虫和有害昆虫进行实时分类。高分辨率摄像头和传感设备持续采集环境与作物数据，并将数据输入物联网系统，在云边计算系统中进行处理。通过配备激光发射器的机器人平台实现高精度除草，且不伤害邻近作物。此外，利用计算机视觉和环境监测来识别空中昆虫蜂群，并通过激光束和ESS技术实现对昆虫蜂群的自动攻击。研究采用多个农业区块对玉米、小麦、大豆和蔬菜作物进行了实验评估。结果表明，与当前做法相比，杂草检测、病虫害识别、作物保护的准确性显著提高，作业成本得以节省。所提出的系统在杂草分类中达到97.6%的准确率，昆虫检测准确率为96.8%，农药使用量减少42.3%。此外，作物产量提高18.7%，作业成本下降28.4%。总体而言，结果表明人工智能驱动的物联网能够显著增强精准农业并促进可持续粮食生产。","International Journal of Drug Delivery Technology",75,{"impact":183,"substance":184,"depth":185,"authority":186,"freshness":13,"relevant":52,"comment":187},18,22,17,8,"论文给出97.6%杂草识别率、42.3%农药减量等量化结论，方法新颖且数据完整，但期刊权威性一般，属细分领域技术进展。",[189],{"name":180,"url":177},[107,108,137,191,111],"杂草识别",{"code":26,"slug":27,"name":28,"path":29,"parent_code":9},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":195},{"candidates":196,"agricultural_evidence":40,"reason":56},[197,206,211,220,224,231,238,245],{"code":38,"score":183,"supported":40,"title":198,"abstract":200,"tags":205},[199],"deep learning",[201,202,203,199,122,204],"深度学习","计算机视觉","病虫害识别","computer vision",[108],{"code":134,"score":183,"supported":40,"title":207,"abstract":208,"tags":210},[139],[137,138,139,209],"edge computing",[137],{"code":212,"score":13,"supported":40,"title":213,"abstract":214,"tags":219},"F4",[],[215,216,217,218],"农药","除草","pesticide","crop protection",[191],{"code":142,"score":143,"supported":40,"title":221,"abstract":222,"tags":223},[],[146,147],[107],{"code":225,"score":87,"supported":40,"title":226,"abstract":227,"tags":230},"A2",[],[228,229],"小麦","wheat",[],{"code":232,"score":87,"supported":40,"title":233,"abstract":234,"tags":237},"A3",[],[235,236],"玉米","maize",[],{"code":239,"score":87,"supported":40,"title":240,"abstract":241,"tags":244},"A4",[],[242,243],"大豆","soybean",[],{"code":246,"score":87,"supported":40,"title":247,"abstract":248,"tags":251},"B1",[],[249,250],"蔬菜","vegetable",[],[253,254],"AI IoT 激光除草 病虫害识别","深度学习 精准农业 农药减量","AIIoT激光除草病虫害识别-5556","10.25258\u002Fijddt.16.76s.32",{"doi":256,"openalex_id":258,"authors":259,"venue":180,"cited_by_count":15,"oa_url":177,"card":260,"direction":172,"ingested_from":84},"W7223145687",[],{"tldr":261,"method":262,"finding":263,"direction":172,"opportunity":264},"提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。","IoT传感器、CNN视觉分类、无人机\u002F机器人平台与激光除草，云边协同处理。","杂草分类准确率97.6%、害虫识别96.8%，农药减42.3%，增产18.7%。","可探索激光除草对非靶标生物与土壤的长期生态影响，及多作物跨域模型泛化。","2026-10-22T11:20:18.540359Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":95,"source_url":269,"published_at":272,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":283,"secondary_topics":284,"classification":285,"search_phrases":307,"slug":310,"view_count":15,"doi":311,"paper":312,"created_at":327},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","2026-10-20T00:00:00Z",70,{"impact":99,"substance":275,"depth":185,"authority":99,"freshness":102,"relevant":52,"comment":276},20,"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[278],{"name":95,"url":269},[108,128,280,281,282],"GIS","野生动物保护","人兽冲突",{"code":26,"slug":27,"name":28,"path":29,"parent_code":9},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":286},{"candidates":287,"agricultural_evidence":40,"reason":56},[288,292,297,302],{"code":38,"score":13,"supported":40,"title":289,"abstract":290,"tags":291},[],[120,121,43],[108],{"code":46,"score":13,"supported":40,"title":293,"abstract":294,"tags":296},[],[22,280,295],"geographic information",[280],{"code":125,"score":298,"supported":40,"title":299,"abstract":300,"tags":301},4,[],[128],[128],{"code":303,"score":52,"supported":14,"title":304,"abstract":305,"tags":306},"H6",[],[],[281],[308,309],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":311,"openalex_id":313,"authors":314,"venue":95,"cited_by_count":15,"oa_url":269,"card":321,"direction":170,"ingested_from":84},"W7222999966",[315,317,319],{"name":316,"orcid":9},"Bhutia Dechen Doma",{"name":318,"orcid":9},"Swarup Das",{"name":320,"orcid":9},"Mandal Rakesh Kumar",{"tldr":322,"method":323,"finding":324,"direction":325,"opportunity":326},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","农业遥感与作物表型","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","2026-10-21T23:30:43.936453Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":340,"tags":342,"primary_topic":346,"secondary_topics":347,"classification":348,"search_phrases":386,"slug":389,"view_count":15,"doi":390,"paper":391,"created_at":402},5403,"RS-GIS Based Dss (Decision Support System) for Land evaluation and Land resource management – (case study Amol District)","https:\u002F\u002Fdoi.org\u002F10.71500\u002Fthesis.2026.519222","Creating a proper planting pattern according to land talent identification, cannot be achieved without considering socioeconomic conditions and detailed holistic. This issue is considered by development of rice which is one of the main world’s most important agricultural products in rural areas and ensures their food security. In the country rice is considered as a strategic plant. This is among sustainable resources of area and attention to its development and expanding its cultivation causes farmers’ confidence and ensures economic stability of the region. On the other hand, crop insurance is same for all land, because statistical information on agricultural lands are most obtained through traditional methods based on the opinion of experts and individuals and collect this information always accompanied with error. Accordingly annually huge costs are paid for insured loss of agricultural lands Thus land evaluation, identification and resolution of land based on its capabilities by taking the capacity of the soil and environmental risks into account is essential for land management. This study presents a spatial decision support system (SDSS) to land evaluation and identification of suitable areas for rice cultivation in the studied area. After gathering data and field studies, various layers of criteria affecting decisions were created in GIS environment. Development of multi-criteria decision-making techniques (MCDM) is done using Analytical Hierarchy Process AHP, fuzzy logic and artificial neural networks. The comparison and integration of models (model compilation and standardization of fuzzy AHP, FAHP, fuzzy standardization and FAHP) was also carried out. To review and matching between fitness maps obtained from different models and the observed value of kappa coefficient was calculated. Neural network model with kappa coefficient 0.65 has highest fitness coefficient among land suitability class and the harvest amount and this due to the performance value in determining the classes is predictable in this model. After that, Fuzzy standardization compilation models and FAHP and Fuzzy standardization and AHP with Kappa coefficient of 0.54 and 0.51 respectively showed highest agreement between the land suitability classes and performance. The majority of lands located on non-prone class have high slope and if cultivation increase risk of soil erosion. Investigating area under rice plantation (paddy fields) with very prone and prone lands in different styles showed that almost the majority of this area is currently devoted to rice cultivation. Although neural network model showed higher accuracy than other models, but this assessment model requires data on rice yield and collecting performance rate required accuracy, cost and much time and sometimes land management and environmental hazards affects it.","根据土地适宜性识别来制定合理的种植模式，离不开对社会经济条件和详细整体性因素的考量。水稻作为世界最重要的农产品之一，其发展在农村地区受到重视，并保障了当地的粮食安全。在我国，水稻被视为战略作物。水稻是区域可持续资源之一，关注其发展并扩大种植面积能够增强农民信心，保障区域经济稳定。另一方面，作物保险对所有土地采取统一标准，因为农业用地的统计信息大多基于专家和个人意见通过传统方法获取，且此类信息的采集始终伴随着误差。因此，每年为农业用地保险损失支付巨额费用。有鉴于此，土地评价、识别及基于土地能力的土地问题解决，必须将土壤容量和环境风险纳入考量，这对土地管理至关重要。本研究提出了一种空间决策支持系统（SDSS），用于研究区域的土地评价和水稻适宜种植区识别。在收集数据和实地调研之后，于GIS环境中创建了影响决策的各准则图层。多准则决策技术（MCDM）的开发采用了层次分析法（AHP）、模糊逻辑和人工神经网络。同时进行了模型的比较与集成（模型编制与模糊AHP标准化、FAHP、模糊标准化与FAHP）。为审查和匹配不同模型获得的适宜性图件，计算了观测值与kappa系数。神经网络模型的kappa系数为0.65，在土地适宜性等级与产量之间具有最高的拟合系数，这归因于该模型在确定等级方面的性能值具有可预测性。其后，模糊标准化编制模型与FAHP、模糊标准化与AHP的kappa系数分别为0.54和0.51，在土地适宜性等级与产量之间表现出最高的一致性。位于非适宜等级的大多数土地具有高坡度，若扩大种植将增加土壤侵蚀风险。对不同适宜性等级下水稻种植区（稻田）的调查……","Test","2026-10-19T00:00:00Z",50,{"impact":186,"substance":100,"depth":338,"authority":39,"freshness":186,"relevant":52,"comment":339},15,"案例研究有一定方法参考价值，但来源不明、地域局限，不足以进入每日精选。",[341],{"name":334,"url":331},[343,128,280,344,345],"决策支持系统","水稻种植","土地评价",{"code":26,"slug":27,"name":28,"path":29,"parent_code":9},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":349},{"candidates":350,"agricultural_evidence":40,"reason":56},[351,355,359,368,375,382],{"code":38,"score":99,"supported":40,"title":352,"abstract":353,"tags":354},[43],[43],[343],{"code":46,"score":99,"supported":40,"title":356,"abstract":357,"tags":358},[280],[280],[280],{"code":360,"score":13,"supported":40,"title":361,"abstract":362,"tags":367},"A1",[],[363,364,365,366],"水稻","稻田","rice","paddy",[344],{"code":369,"score":87,"supported":40,"title":370,"abstract":371,"tags":374},"A8",[],[372,373],"粮食安全","food security",[],{"code":376,"score":87,"supported":40,"title":377,"abstract":378,"tags":381},"H2",[],[379,380],"土壤","soil",[],{"code":125,"score":52,"supported":14,"title":383,"abstract":384,"tags":385},[],[],[128],[387,388],"Amol District 水稻 土地适宜性","RS-GIS 决策支持系统 土地评价","AmolDistrict水稻土地适宜性-5403","10.71500\u002Fthesis.2026.519222",{"doi":390,"openalex_id":392,"authors":393,"venue":334,"cited_by_count":15,"oa_url":331,"card":397,"direction":170,"ingested_from":84},"W7222963628",[394],{"name":395,"orcid":396},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":398,"method":399,"finding":400,"direction":325,"opportunity":401},"构建基于RS-GIS的空间决策支持系统，评估伊朗Amol地区水稻种植适宜性。","GIS叠加多准则决策、AHP、模糊逻辑与人工神经网络，用Kappa系数验证。","神经网络模型Kappa系数0.65最高，土地适宜性与产量吻合最好。","可将该SDSS与实时遥感、作物保险定损结合，发展动态土地适宜性监测。","2026-10-20T23:30:40.596410Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":409,"source_url":406,"published_at":410,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":411,"score_detail":412,"sources":414,"tags":416,"primary_topic":420,"secondary_topics":421,"classification":422,"search_phrases":451,"slug":454,"view_count":15,"doi":455,"paper":456,"created_at":476},5166,"Application of the SCS–CN Method Integrated with GIS and Remote Sensing for Watershed Runoff Estimation: A Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i105735","Accurate estimation of runoff is crucial for watershed planning, soil and water conservation, flood mitigation, and the management of ungauged watersheds. The Soil Conservation Service Curve Number (SCS-CN) method has been extensively used because of its minimal input requirements and its ability to model the spatial influence of land use, soil properties, and hydrologic soil groups. This review compiles applications of the GIS-based SCS-CN method reported for watersheds in semi-arid and monsoon-affected countries such as India, Saudi Arabia, Afghanistan, China, Iraq, and Ethiopia. The reviewed literature employed rainfall data, land-use\u002Fland-cover data, soil maps, hydrologic soil groups, digital elevation models, and watershed morphometry to calculate curve numbers and direct runoff. The reported CN values varied substantially, from about 40 under dry conditions to more than 90 under urban, rocky, agricultural, or poorly infiltrating conditions. Runoff estimates ranged from relatively low values in arid catchments to more than 1,300 mm per year in high-rainfall watersheds. Several studies reported high R2 correlations between observed and estimated runoff, typically ranging from 0.73 to 0.98. Important limitations identified in the review include the sensitivity of results to the choice of curve number, antecedent moisture condition, initial abstraction assumption, land-use classification, soil-group assignment, rainfall representation, and the lack of observed discharge data. The overall design of the SCS-CN method and its integration with RS and GIS offer a practical approach to assessing spatial runoff and prioritising watersheds, but the method requires local calibration, uncertainty analysis, and validation against observed rainfall–runoff data.","径流的准确估算对于流域规划、水土保持、洪水缓解以及无资料流域的管理至关重要。土壤保持服务曲线数（SCS-CN）方法因其输入需求少、能够模拟土地利用、土壤性质和水文土壤组的空间影响而被广泛使用。本综述汇编了基于GIS的SCS-CN方法在半干旱和受季风影响国家（如印度、沙特阿拉伯、阿富汗、中国、伊拉克和埃塞俄比亚）流域的应用报道。所综述的文献利用降雨数据、土地利用\u002F土地覆盖数据、土壤图、水文土壤组、数字高程模型和流域形态计量来计算曲线数和直接径流。报道的CN值差异很大，从干旱条件下的约40到城市、岩石、农业或入渗不良条件下的90以上不等。径流估算值从干旱集水区的相对较低值到高降雨流域的年径流量超过1300 mm不等。多项研究报告了观测径流与估算径流之间的高R²相关性，通常在0.73至0.98之间。本综述识别出的重要局限性包括：结果对曲线数选择、前期土壤湿润条件、初始损失假设、土地利用分类、土壤组赋值、降雨代表性以及缺乏实测流量数据的敏感性。SCS-CN方法的总体设计及其与遥感和GIS的集成，为评估空间径流和流域优先排序提供了一种实用方法，但该方法需要本地校准、不确定性分析，并依据实测降雨—径流数据进行验证。","International Journal of Environment and Climate Change","2026-10-16T00:00:00Z",63,{"impact":186,"substance":183,"depth":100,"authority":99,"freshness":102,"relevant":52,"comment":413},"综述系统梳理了GIS与遥感集成的SCS-CN径流估算方法及其局限，对农业信息化中的流域水资源与水土保持管理有参考价值，但属方法综述、影响面有限，未达每日精选门槛。",[415],{"name":409,"url":406},[128,280,417,418,419],"水土保持","流域管理","水文模型",{"code":26,"slug":27,"name":28,"path":29,"parent_code":9},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":423},{"candidates":424,"agricultural_evidence":40,"reason":56},[425,429,433,437,441,446],{"code":125,"score":99,"supported":40,"title":426,"abstract":427,"tags":428},[129],[128],[128],{"code":46,"score":99,"supported":40,"title":430,"abstract":431,"tags":432},[280],[280],[280],{"code":376,"score":87,"supported":40,"title":434,"abstract":435,"tags":436},[],[379,380],[],{"code":303,"score":298,"supported":40,"title":438,"abstract":439,"tags":440},[],[417],[417],{"code":150,"score":39,"supported":40,"title":442,"abstract":443,"tags":445},[],[444],"干旱",[],{"code":447,"score":52,"supported":14,"title":448,"abstract":449,"tags":450},"H3",[],[],[419],[452,453],"SCS-CN GIS 遥感 径流估算","水土保持 水文模型 流域管理 遥感","SCS-CNGIS遥感径流估算-5166","10.9734\u002Fijecc\u002F2026\u002Fv16i105735",{"doi":455,"openalex_id":457,"authors":458,"venue":409,"cited_by_count":15,"oa_url":406,"card":471,"direction":325,"ingested_from":84},"W7222632567",[459,461,463,465,467,469],{"name":460,"orcid":9},"Sudhir Lad",{"name":462,"orcid":9},"R. D. Bansod",{"name":464,"orcid":9},"Bhaupatil Gavit",{"name":466,"orcid":9},"Sachin Nandgude",{"name":468,"orcid":9},"Shailendra Gadge",{"name":470,"orcid":9},"Mahesh Patil",{"tldr":472,"method":473,"finding":474,"direction":325,"opportunity":475},"综述GIS与遥感集成的SCS-CN法在半干旱与季风区流域径流估算的应用。","汇总印度、中国等国文献，用降雨、土地利用、土壤图、DEM与流域形态计算CN和径流","CN约40至90以上，年径流差异大，R²多在0.73至0.98，但结果对CN与前期湿度等敏感。","可研究CN本地化校准与不确定性量化，并结合实测径流验证，提升无资料流域径流估算可靠性。","2026-10-17T23:30:28.373754Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":482,"content":9,"source_name":483,"source_url":480,"published_at":484,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":485,"score_detail":486,"sources":489,"tags":491,"primary_topic":493,"secondary_topics":494,"classification":495,"search_phrases":525,"slug":528,"view_count":52,"doi":529,"paper":530,"created_at":542},5074,"QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI","https:\u002F\u002Fdoi.org\u002F10.67547\u002Ffanistiqbollari.2026.876","The article examines a methodology for integrating Smart Agriculture technologies into the teaching of specialized subjects at agricultural higher education institutions. The study analyzes the didactic potential of the Internet of Things, sensor monitoring, GIS, remote sensing, artificial intelligence, smart irrigation and digital data analytics.","本文探讨了将智慧农业技术融入农业高等院校专业课程教学的方法论。研究分析了物联网、传感器监测、地理信息系统、遥感、人工智能、智能灌溉和数字数据分析在教学中的潜力。","Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal","2026-10-15T00:00:00Z",49,{"impact":186,"substance":487,"depth":101,"authority":87,"freshness":186,"relevant":52,"comment":488},14,"乌兹别克语论文探讨智慧农业技术融入农业高校专业教学的路径，属农业信息化人才培养方向，但为境外一般期刊、影响面有限，暂不宜进入每日精选。",[490],{"name":483,"url":480},[107,108,137,128,492],"农业教育",{"code":26,"slug":27,"name":28,"path":29,"parent_code":9},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":496},{"candidates":497,"agricultural_evidence":40,"reason":56},[498,503,507,511,517,521],{"code":142,"score":338,"supported":40,"title":499,"abstract":501,"tags":502},[500],"smart agriculture",[107,500],[107],{"code":125,"score":143,"supported":40,"title":504,"abstract":505,"tags":506},[],[128,129],[128],{"code":134,"score":143,"supported":40,"title":508,"abstract":509,"tags":510},[],[137,138],[137],{"code":447,"score":87,"supported":40,"title":512,"abstract":513,"tags":516},[],[514,515],"灌溉","irrigation",[],{"code":46,"score":87,"supported":40,"title":518,"abstract":519,"tags":520},[],[22,280],[],{"code":38,"score":298,"supported":40,"title":522,"abstract":523,"tags":524},[],[122],[108],[526,527],"农业人工智能 农业教育 智慧农业 物联网","农业人工智能 农业教育","农业人工智能农业教育智慧农业物联网-5074","10.67547\u002Ffanistiqbollari.2026.876",{"doi":529,"openalex_id":531,"authors":532,"venue":483,"cited_by_count":15,"oa_url":536,"card":537,"direction":172,"ingested_from":84},"W7221997102",[533],{"name":534,"orcid":535},"O'G'ILOY SADULLO QIZI SHAMSIYEV","https:\u002F\u002Forcid.org\u002F0009-0003-4706-6488","https:\u002F\u002Ffanistiqbollari.uz\u002Fstorage\u002Farticle876.pdf",{"tldr":538,"method":539,"finding":540,"direction":82,"opportunity":541},"研究将智慧农业技术融入农业高校专业课程教学的方法论。","分析物联网、传感器、GIS、遥感、AI等技术的教学潜力。","智慧农业技术可有效支撑农业专业课程的教学方法设计。","可实证检验该教学法对学生技能与就业的影响，并开发配套课程资源。","2026-10-16T23:30:07.890026Z"]